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Machine Learning (ML) in low-data settings remains an underappreciated yet crucial problem.
Smote: synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
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Semi-Supervised Learning
Chapelle, O., Schölkopf, B., and Zien, A. (eds.) · 2006
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UCI machine learning repository, 2007
Asuncion, A. and Newman, D · 2007
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2009
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Active learning literature survey
Settles, B · 2009
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Bayesian active learning for classification and preference learning
Houlsby, N., Huszár, F., Ghahramani, Z., and Lengyel, M · 2011
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies
Pocock, S. J., Ariti, C. A., McMurray, J. J., Maggioni, A., Køber, L., Squire, I. B., Swedberg, K., Dobson, J., Poppe, K. K., Whalley, G. A., et al · 2013
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Openml: Networked science in machine learning
Vanschoren, J., van Rijn, J. N., Bischl, B., and Torgo, L · 2013
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Certifying and removing disparate impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S · 2015
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Machine bias
Angwin, J., Larson, J., Kirchner, L., and Mattu, S · 2016
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The surveillance, epidemiology and end results (SEER) program and pathology: towards strengthening the critical relationship
Duggan, M. A., Anderson, W. F., Altekruse, S., Penberthy, L., and Sherman, M. E · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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A closer look at memorization in deep networks
Arpit, D., Jastrzkebski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al · 2017
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A convex framework for fair regression
Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., Neel, S., and Roth, A · 2017
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Optimized pre-processing for discrimination prevention
Calmon, F., Wei, D., Vinzamuri, B., Natesan Ramamurthy, K., and Varshney, K. R · 2017
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Real-valued (medical) time series generation with recurrent conditional gans
Esteban, C., Hyland, S. L., and Rätsch, G · 2017
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The five factor model of personality and evaluation of drug consumption risk
Fehrman, E., Muhammad, A. K., Mirkes, E. M., Egan, V., and Gorban, A. N · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning
Lemaître, G., Nogueira, F., and Aridas, C. K · 2017
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Machine learning for the developing world
De-Arteaga, M., Herlands, W., Neill, D. B., and Dubrawski, A · 2018
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On the relationship between data efficiency and error for uncertainty sampling
Mussmann, S. and Liang, P · 2018
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Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
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Mitigating unwanted biases with adversarial learning
Zhang, B. H., Lemoine, B., and Mitchell, M · 2018
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Arora, S., Du, S., Hu, W., Li, Z., and Wang, R · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Kirsch, A., Van Amersfoort, J., and Gal, Y · 2019
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Tanaka, F. H. K. D. S. and Aranha, C · 2019
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A survey on semi-supervised learning
van Engelen, J. E. and Hoos, H. H · 2019
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Modeling tabular data using conditional gan
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K · 2019
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Artificial intelligence in health care: laying the foundation for responsible, sustainable, and inclusive innovation in low-and middle-income countries
Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Cadeddu, S. B. M., Savoldelli, M., Samri, M. A., Ag Ahmed, M. A., Fleet, R., and Fortin, J.-P · 2020
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Ethnic and regional variations in hospital mortality from covid-19 in brazil: a cross-sectional observational study
Baqui, P., Bica, I., Marra, V., Ercole, A., and van Der Schaar, M · 2020
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Generation and evaluation of synthetic patient data
Goncalves, A., Ray, P., Soper, B., Stevens, J., Coyle, L., and Sales, A. P · 2020
Cited alongside, same era.
Uncertainty quantification using bayesian neural networks in classification: Application to biomedical image segmentation
Kwon, Y., Won, J.-H., Kim, B. J., and Paik, M. C · 2020
Cited alongside, same era.
Learning over-parametrized two-layer neural networks beyond ntk
Li, Y., Ma, T., and Zhang, H. R · 2020
Cited alongside, same era.
Benchmarking data augmentation techniques for tabular data
Machado, P., Fernandes, B., and Novais, P · 2022
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Graph-conditioned mlp for high-dimensional tabular biomedical data
Margeloiu, A., Simidjievski, N., Lio, P., and Jamnik, M · 2022
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How to measure uncertainty in uncertainty sampling for active learning
Nguyen, V.-L., Shaker, M. H., and Hüllermeier, E · 2022
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Tabular data: Deep learning is not all you need
Shwartz-Ziv, R. and Armon, A · 2022
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Artificial intelligence in africa: Emerging challenges
Ade-Ibijola, A. and Okonkwo, C · 2023
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Globalizing fairness attributes in machine learning: A case study on health in africa
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Artificial intelligence in low-and middle-income countries: innovating global health radiology
Mollura, D. J., Culp, M. P., Pollack, E., Battino, G., Scheel, J. R., Mango, V. L., Elahi, A., Schweitzer, A., and Dako, F · 2020
Cited alongside, same era.
Artificial intelligence for healthcare in africa
Owoyemi, A., Owoyemi, J., Osiyemi, A., and Boyd, A · 2020
Cited alongside, same era.
Generalizing from a few examples: A survey on few-shot learning
Wang, Y., Yao, Q., Kwok, J. T., and Ni, L. M · 2020
Cited alongside, same era.
A comprehensive survey on transfer learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q · 2020
Cited alongside, same era.
Assuring the machine learning lifecycle: Desiderata, methods, and challenges
Ashmore, R., Calinescu, R., and Paterson, C · 2021
Cited alongside, same era.
A brief review of domain adaptation
Farahani, A., Voghoei, S., Rasheed, K., and Arabnia, H. R · 2021
Cited alongside, same era.
Datasheets for datasets
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., and Crawford, K · 2021
Cited alongside, same era.
Asiedu, M. N., Dieng, A., Oppong, A., Nagawa, M., Koyejo, S., and Heller, K · 2023
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Generative adversarial networks for data augmentation
Biswas, A., Nasim, M., Imran, A., Sejuty, A. T., Fairooz, F., Puppala, S., and Talukder, S · 2023
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Language models are realistic tabular data generators
Borisov, V., Sessler, K., Leemann, T., Pawelczyk, M., and Kasneci, G · 2023
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Bias and fairness in large language models: A survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., and Ahmed, N. K · 2023
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Synthesizing electronic health records for predictive models in low-middle-income countries (lmics)
Ghosheh, G. O., Thwaites, C. L., and Zhu, T · 2023
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Tabllm: Few-shot classification of tabular data with large language models
Hegselmann, S., Buendia, A., Lang, H., Agrawal, M., Jiang, X., and Sontag, D · 2023
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Mistral 7b, 2023
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2023
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Benchmarking tabular representation models in transfer learning settings
Jin, Q. and Ucar, T · 2023
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Ethics of large language models in medicine and medical research
Li, H., Moon, J. T., Purkayastha, S., Celi, L. A., Trivedi, H., and Gichoya, J. W · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 2023
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Tuning language models as training data generators for augmentation-enhanced few-shot learning
Meng, Y., Michalski, M., Huang, J., Zhang, Y., Abdelzaher, T., and Han, J · 2023
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Large language models as general pattern machines
Mirchandani, S., Xia, F., Florence, P., Ichter, B., Driess, D., Arenas, M. G., Rao, K., Sadigh, D., and Zeng, A · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., and Launay, J · 2023
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Synthcity: facilitating innovative use cases of synthetic data in different data modalities, 2023
Qian, Z., Cebere, B.-C., and van der Schaar, M · 2023
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Neural machine translation for low-resource languages: A survey
Ranathunga, S., Lee, E.-S. A., Prifti Skenduli, M., Shekhar, R., Alam, M., and Kaur, R · 2023
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Enabling tabular deep learning when d \ ≫ n \backslash\gg n with an auxiliary knowledge graph
Ruiz, C., Ren, H., Huang, K., and Leskovec, J · 2023
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Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al · 2023
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Llama: Open and efficient foundation language models, 2023
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G · 2023
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Enhancing small tabular clinical trial dataset through hybrid data augmentation: Combining smote and wcgan-gp
Wang, W. and Pai, T.-W · 2023
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Larger language models do in-context learning differently
Wei, J., Wei, J., Tay, Y., Tran, D., Webson, A., Lu, Y., Chen, X., Liu, H., Huang, D., Zhou, D., et al · 2023
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2023
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Data-centric artificial intelligence: A survey
Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., and Hu, X · 2023
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
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